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Zero
Running
on
Zero
| import torch | |
| import torch.nn as nn | |
| from models.modules.aspp import ASPP, ASPPDeformable | |
| from models.modules.attentions import PSA, SGE | |
| from config import Config | |
| config = Config() | |
| class BasicDecBlk(nn.Module): | |
| def __init__(self, in_channels=64, out_channels=64, inter_channels=64): | |
| super(BasicDecBlk, self).__init__() | |
| inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64 | |
| self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1) | |
| self.relu_in = nn.ReLU(inplace=True) | |
| if config.dec_att == 'ASPP': | |
| self.dec_att = ASPP(in_channels=inter_channels) | |
| elif config.dec_att == 'ASPPDeformable': | |
| self.dec_att = ASPPDeformable(in_channels=inter_channels) | |
| self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1) | |
| self.bn_in = nn.BatchNorm2d(inter_channels) | |
| self.bn_out = nn.BatchNorm2d(out_channels) | |
| def forward(self, x): | |
| x = self.conv_in(x) | |
| x = self.bn_in(x) | |
| x = self.relu_in(x) | |
| if hasattr(self, 'dec_att'): | |
| x = self.dec_att(x) | |
| x = self.conv_out(x) | |
| x = self.bn_out(x) | |
| return x | |
| class ResBlk(nn.Module): | |
| def __init__(self, in_channels=64, out_channels=None, inter_channels=64): | |
| super(ResBlk, self).__init__() | |
| if out_channels is None: | |
| out_channels = in_channels | |
| inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64 | |
| self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1) | |
| self.bn_in = nn.BatchNorm2d(inter_channels) | |
| self.relu_in = nn.ReLU(inplace=True) | |
| if config.dec_att == 'ASPP': | |
| self.dec_att = ASPP(in_channels=inter_channels) | |
| elif config.dec_att == 'ASPPDeformable': | |
| self.dec_att = ASPPDeformable(in_channels=inter_channels) | |
| self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1) | |
| self.bn_out = nn.BatchNorm2d(out_channels) | |
| self.conv_resi = nn.Conv2d(in_channels, out_channels, 1, 1, 0) | |
| def forward(self, x): | |
| _x = self.conv_resi(x) | |
| x = self.conv_in(x) | |
| x = self.bn_in(x) | |
| x = self.relu_in(x) | |
| if hasattr(self, 'dec_att'): | |
| x = self.dec_att(x) | |
| x = self.conv_out(x) | |
| x = self.bn_out(x) | |
| return x + _x | |
| class HierarAttDecBlk(nn.Module): | |
| def __init__(self, in_channels=64, out_channels=None, inter_channels=64): | |
| super(HierarAttDecBlk, self).__init__() | |
| if out_channels is None: | |
| out_channels = in_channels | |
| inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64 | |
| self.split_y = 8 # must be divided by channels of all intermediate features | |
| self.split_x = 8 | |
| self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, 1) | |
| self.psa = PSA(inter_channels*self.split_y*self.split_x, S=config.batch_size) | |
| self.sge = SGE(groups=config.batch_size) | |
| if config.dec_att == 'ASPP': | |
| self.dec_att = ASPP(in_channels=inter_channels) | |
| elif config.dec_att == 'ASPPDeformable': | |
| self.dec_att = ASPPDeformable(in_channels=inter_channels) | |
| self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1) | |
| def forward(self, x): | |
| x = self.conv_in(x) | |
| N, C, H, W = x.shape | |
| x_patchs = x.reshape(N, -1, H//self.split_y, W//self.split_x) | |
| # Hierarchical attention: group attention X patch spatial attention | |
| x_patchs = self.psa(x_patchs) # Group Channel Attention -- each group is a single image | |
| x_patchs = self.sge(x_patchs) # Patch Spatial Attention | |
| x = x.reshape(N, C, H, W) | |
| if hasattr(self, 'dec_att'): | |
| x = self.dec_att(x) | |
| x = self.conv_out(x) | |
| return x | |